opencv-模板匹配

参考：

1、http://docs.opencv.org/3.3.0/  官方文档api

2、http://docs.opencv.org/3.3.0/d6/d00/tutorial_py_root.html 官方英文教程

3、https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_tutorials.html

4、https://github.com/makelove/OpenCV-Python-Tutorial# 进阶教程

5、https://docs.opencv.org/3.3.0/index.html 
官方英文教程

6、https://github.com/abidrahmank/OpenCV2-Python-Tutorials

7、https://www.learnopencv.com/

8、http://answers.opencv.org/questions/ OpenCV论坛

9、https://github.com/opencv/opencv  
官方github

10、https://github.com/abidrahmank/OpenCV2-Python-Tutorials

注：安装的版本
opencv_python-3.3.0-cp36-cp36m-win_amd64.whl

参考：https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_tutorials.html

OpenCV中的模板匹配

在这里，作为一个例子，我们将在他的照片中搜索梅西的脸。 所以我创建了一个如下的模板：



我们将尝试所有的比较方法，以便我们可以看到他们的结果如何：

import cv2
import numpy as np
from matplotlib import pyplot as plt

img = cv2.imread('messi5.jpg',0)
img2 = img.copy()
template = cv2.imread('messi_face.jpg',0)
w, h = template.shape[::-1]

# All the 6 methods for comparison in a list
methods = ['cv2.TM_CCOEFF', 'cv2.TM_CCOEFF_NORMED', 'cv2.TM_CCORR',
'cv2.TM_CCORR_NORMED', 'cv2.TM_SQDIFF', 'cv2.TM_SQDIFF_NORMED']

for meth in methods:
img = img2.copy()
method = eval(meth)

# Apply template Matching
res = cv2.matchTemplate(img,template,method)
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)

# If the method is TM_SQDIFF or TM_SQDIFF_NORMED, take minimum
if method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED]:
top_left = min_loc
else:
top_left = max_loc
bottom_right = (top_left[0] + w, top_left[1] + h)

cv2.rectangle(img,top_left, bottom_right, 255, 2)

plt.subplot(121),plt.imshow(res,cmap = 'gray')
plt.title('Matching Result'), plt.xticks([]), plt.yticks([])
plt.subplot(122),plt.imshow(img,cmap = 'gray')
plt.title('Detected Point'), plt.xticks([]), plt.yticks([])
plt.suptitle(meth)

plt.show()

See the results below:

cv2.TM_CCOEFF


cv2.TM_CCOEFF_NORMED


cv2.TM_CCORR


cv2.TM_CCORR_NORMED


cv2.TM_SQDIFF


cv2.TM_SQDIFF_NORMED



You can see that the result using cv2.TM_CCORR is not good as we expected.

模板匹配多个对象

在上一节中，我们搜索了Messi脸部的图像，在图像中只出现一次。 假设您正在搜索具有多次出现的对象，cv2.minMaxLoc（）将不会给您所有的位置。 在这种情况下，我们将使用阈值。 所以在这个例子中，我们将使用着名的游戏马里奥的屏幕截图，我们将在其中找到硬币。

import cv2
import numpy as np
from matplotlib import pyplot as plt

img_rgb = cv2.imread('mario.png')
img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY)
template = cv2.imread('mario_coin.png',0)
w, h = template.shape[::-1]

res = cv2.matchTemplate(img_gray,template,cv2.TM_CCOEFF_NORMED)
threshold = 0.8
loc = np.where( res >= threshold)
for pt in zip(*loc[::-1]):
cv2.rectangle(img_rgb, pt, (pt[0] + w, pt[1] + h), (0,0,255), 2)

# cv2.imwrite('res.png',img_rgb)
cv2.imshow('img_rgb',img_rgb)
cv2.waitKey(0)
cv2.destroyAllWindows()